Overview

Dataset statistics

Number of variables21
Number of observations45379
Missing cells0
Missing cells (%)0.0%
Duplicate rows20
Duplicate rows (%)< 0.1%
Total size in memory8.5 MiB
Average record size in memory195.3 B

Variable types

Categorical9
Numeric8
Unsupported3
DateTime1

Alerts

Dataset has 20 (< 0.1%) duplicate rowsDuplicates
belongs_to_collection has a high cardinality: 1699 distinct valuesHigh cardinality
genres has a high cardinality: 4068 distinct valuesHigh cardinality
original_language has a high cardinality: 93 distinct valuesHigh cardinality
overview has a high cardinality: 44234 distinct valuesHigh cardinality
spoken_languages has a high cardinality: 1842 distinct valuesHigh cardinality
tagline has a high cardinality: 20270 distinct valuesHigh cardinality
title has a high cardinality: 42197 distinct valuesHigh cardinality
name_collection has a high cardinality: 1696 distinct valuesHigh cardinality
budget is highly overall correlated with revenueHigh correlation
revenue is highly overall correlated with budget and 1 other fieldsHigh correlation
vote_count is highly overall correlated with revenueHigh correlation
belongs_to_collection is highly imbalanced (86.1%)Imbalance
original_language is highly imbalanced (67.7%)Imbalance
spoken_languages is highly imbalanced (61.4%)Imbalance
status is highly imbalanced (96.6%)Imbalance
name_collection is highly imbalanced (86.1%)Imbalance
return is highly skewed (γ1 = 138.3340992)Skewed
title is uniformly distributedUniform
popularity is an unsupported type, check if it needs cleaning or further analysisUnsupported
production_companies is an unsupported type, check if it needs cleaning or further analysisUnsupported
production_countries is an unsupported type, check if it needs cleaning or further analysisUnsupported
budget has 36493 (80.4%) zerosZeros
revenue has 37972 (83.7%) zerosZeros
runtime has 1784 (3.9%) zerosZeros
vote_average has 2950 (6.5%) zerosZeros
vote_count has 2852 (6.3%) zerosZeros
return has 40005 (88.2%) zerosZeros

Reproduction

Analysis started2023-07-11 17:35:06.206067
Analysis finished2023-07-11 17:35:37.357543
Duration31.15 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

belongs_to_collection
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct1699
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
{'name':'sin datos' }
40888 
{'id': 415931, 'name': 'The Bowery Boys', 'poster_path': '/q6sA4bzMT9cK7EEmXYwt7PNrL5h.jpg', 'backdrop_path': '/foe3kuiJmg5AklhtD3skWbaTMf2.jpg'}
 
29
{'id': 421566, 'name': 'Totò Collection', 'poster_path': '/4ayJsjC3djGwU9eCWUokdBWvdLC.jpg', 'backdrop_path': '/jaUuprubvAxXLAY5hUfrNjxccUh.jpg'}
 
27
{'id': 645, 'name': 'James Bond Collection', 'poster_path': '/HORpg5CSkmeQlAolx3bKMrKgfi.jpg', 'backdrop_path': '/6VcVl48kNKvdXOZfJPdarlUGOsk.jpg'}
 
26
{'id': 96887, 'name': 'Zatôichi: The Blind Swordsman', 'poster_path': '/8Q31DAtmFJjhFTwQGXghBUCgWK2.jpg', 'backdrop_path': '/bY8gLImMR5Pr9PaG3ZpobfaAQ8N.jpg'}
 
26
Other values (1694)
4383 

Length

Max length184
Median length21
Mean length32.915313
Min length8

Characters and Unicode

Total characters1493664
Distinct characters170
Distinct categories13 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique393 ?
Unique (%)0.9%

Sample

1st row{'id': 10194, 'name': 'Toy Story Collection', 'poster_path': '/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg', 'backdrop_path': '/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg'}
2nd row{'name':'sin datos' }
3rd row{'id': 119050, 'name': 'Grumpy Old Men Collection', 'poster_path': '/nLvUdqgPgm3F85NMCii9gVFUcet.jpg', 'backdrop_path': '/hypTnLot2z8wpFS7qwsQHW1uV8u.jpg'}
4th row{'name':'sin datos' }
5th row{'id': 96871, 'name': 'Father of the Bride Collection', 'poster_path': '/nts4iOmNnq7GNicycMJ9pSAn204.jpg', 'backdrop_path': '/7qwE57OVZmMJChBpLEbJEmzUydk.jpg'}

Common Values

ValueCountFrequency (%)
{'name':'sin datos' } 40888
90.1%
{'id': 415931, 'name': 'The Bowery Boys', 'poster_path': '/q6sA4bzMT9cK7EEmXYwt7PNrL5h.jpg', 'backdrop_path': '/foe3kuiJmg5AklhtD3skWbaTMf2.jpg'} 29
 
0.1%
{'id': 421566, 'name': 'Totò Collection', 'poster_path': '/4ayJsjC3djGwU9eCWUokdBWvdLC.jpg', 'backdrop_path': '/jaUuprubvAxXLAY5hUfrNjxccUh.jpg'} 27
 
0.1%
{'id': 645, 'name': 'James Bond Collection', 'poster_path': '/HORpg5CSkmeQlAolx3bKMrKgfi.jpg', 'backdrop_path': '/6VcVl48kNKvdXOZfJPdarlUGOsk.jpg'} 26
 
0.1%
{'id': 96887, 'name': 'Zatôichi: The Blind Swordsman', 'poster_path': '/8Q31DAtmFJjhFTwQGXghBUCgWK2.jpg', 'backdrop_path': '/bY8gLImMR5Pr9PaG3ZpobfaAQ8N.jpg'} 26
 
0.1%
{'id': 37261, 'name': 'The Carry On Collection', 'poster_path': '/2P0HNrYgKDvirV8RCdT1rBSJdbJ.jpg', 'backdrop_path': '/38tF1LJN7ULeZAuAfP7beaPMfcl.jpg'} 25
 
0.1%
{'id': 34055, 'name': 'Pokémon Collection', 'poster_path': '/j5te0YNZAMXDBnsqTUDKIBEt8iu.jpg', 'backdrop_path': '/iGoYKA0TFfgSoZpG2u5viTJMGfK.jpg'} 22
 
< 0.1%
{'id': 413661, 'name': 'Charlie Chan (Sidney Toler) Collection', 'poster_path': '/y0xWQpLRattvypZXF5ZiuipsD2U.jpg', 'backdrop_path': None} 21
 
< 0.1%
{'id': 374509, 'name': 'Godzilla (Showa) Collection', 'poster_path': '/scvwS6k8gIW8w24UcmePQqVL10l.jpg', 'backdrop_path': '/dx9YSup5zEOjxYwG4UkYBVAZIXo.jpg'} 16
 
< 0.1%
{'id': 425164, 'name': 'Dragon Ball Z (Movie) Collection', 'poster_path': '/2VMZ1zRFPnUQtQp5K4WRXvDYBjh.jpg', 'backdrop_path': '/7PcbijxTfwi9vjWEfXdS0ReAw8q.jpg'} 15
 
< 0.1%
Other values (1689) 4284
 
9.4%

Length

2023-07-11T14:35:37.864268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41027
24.3%
name':'sin 40888
24.2%
datos 40888
24.2%
name 4494
 
2.7%
id 4488
 
2.7%
poster_path 4488
 
2.7%
backdrop_path 4488
 
2.7%
collection 3743
 
2.2%
none 1771
 
1.0%
the 1146
 
0.7%
Other values (6636) 21446
12.7%

Most occurring characters

ValueCountFrequency (%)
' 222735
14.9%
123489
 
8.3%
a 107467
 
7.2%
n 98493
 
6.6%
s 91920
 
6.2%
o 65914
 
4.4%
e 65100
 
4.4%
t 64074
 
4.3%
: 58939
 
3.9%
i 56212
 
3.8%
Other values (160) 539321
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 807531
54.1%
Other Punctuation 310135
 
20.8%
Space Separator 123489
 
8.3%
Uppercase Letter 94971
 
6.4%
Decimal Number 56927
 
3.8%
Close Punctuation 45711
 
3.1%
Open Punctuation 45711
 
3.1%
Connector Punctuation 8976
 
0.6%
Dash Punctuation 162
 
< 0.1%
Other Letter 37
 
< 0.1%
Other values (3) 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 107467
13.3%
n 98493
12.2%
s 91920
11.4%
o 65914
8.2%
e 65100
8.1%
t 64074
7.9%
i 56212
7.0%
d 54579
6.8%
m 49913
 
6.2%
p 29059
 
3.6%
Other values (69) 124800
15.5%
Uppercase Letter
ValueCountFrequency (%)
C 7690
 
8.1%
N 5091
 
5.4%
T 4593
 
4.8%
S 4187
 
4.4%
A 3721
 
3.9%
M 3693
 
3.9%
B 3679
 
3.9%
D 3678
 
3.9%
L 3478
 
3.7%
G 3457
 
3.6%
Other values (33) 51704
54.4%
Other Letter
ValueCountFrequency (%)
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
Other values (4) 8
21.6%
Other Punctuation
ValueCountFrequency (%)
' 222735
71.8%
: 58939
 
19.0%
, 13543
 
4.4%
. 7380
 
2.4%
/ 7226
 
2.3%
" 214
 
0.1%
& 52
 
< 0.1%
! 35
 
< 0.1%
* 4
 
< 0.1%
? 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 6788
11.9%
2 6102
10.7%
3 5869
10.3%
4 5779
10.2%
5 5701
10.0%
9 5478
9.6%
8 5451
9.6%
6 5368
9.4%
7 5345
9.4%
0 5046
8.9%
Close Punctuation
ValueCountFrequency (%)
} 45376
99.3%
) 330
 
0.7%
] 5
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
{ 45376
99.3%
( 330
 
0.7%
[ 5
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 160
98.8%
2
 
1.2%
Space Separator
ValueCountFrequency (%)
123489
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8976
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 902088
60.4%
Common 591125
39.6%
Cyrillic 414
 
< 0.1%
Hiragana 15
 
< 0.1%
Hangul 10
 
< 0.1%
Katakana 9
 
< 0.1%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 107467
11.9%
n 98493
10.9%
s 91920
10.2%
o 65914
 
7.3%
e 65100
 
7.2%
t 64074
 
7.1%
i 56212
 
6.2%
d 54579
 
6.1%
m 49913
 
5.5%
p 29059
 
3.2%
Other values (70) 219357
24.3%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
Common
ValueCountFrequency (%)
' 222735
37.7%
123489
20.9%
: 58939
 
10.0%
} 45376
 
7.7%
{ 45376
 
7.7%
, 13543
 
2.3%
_ 8976
 
1.5%
. 7380
 
1.2%
/ 7226
 
1.2%
1 6788
 
1.1%
Other values (24) 51297
 
8.7%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Katakana
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Han
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1492950
> 99.9%
Cyrillic 414
 
< 0.1%
None 246
 
< 0.1%
Hiragana 15
 
< 0.1%
Punctuation 14
 
< 0.1%
Katakana 12
 
< 0.1%
Hangul 10
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 222735
14.9%
123489
 
8.3%
a 107467
 
7.2%
n 98493
 
6.6%
s 91920
 
6.2%
o 65914
 
4.4%
e 65100
 
4.4%
t 64074
 
4.3%
: 58939
 
3.9%
i 56212
 
3.8%
Other values (71) 538607
36.1%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
None
ValueCountFrequency (%)
é 45
18.3%
ä 40
16.3%
ô 35
14.2%
ò 28
11.4%
ö 19
7.7%
ó 14
 
5.7%
ı 14
 
5.7%
í 9
 
3.7%
á 4
 
1.6%
İ 4
 
1.6%
Other values (19) 34
13.8%
Punctuation
ValueCountFrequency (%)
9
64.3%
3
 
21.4%
2
 
14.3%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Katakana
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
CJK
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4232324.6
Minimum0
Maximum3.8 × 108
Zeros36493
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:38.151677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17439317
Coefficient of variation (CV)4.1205056
Kurtosis66.63901
Mean4232324.6
Median Absolute Deviation (MAD)0
Skewness7.1185794
Sum1.9205866 × 1011
Variance3.0412978 × 1014
MonotonicityNot monotonic
2023-07-11T14:35:38.447179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36493
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1213) 6814
 
15.0%
ValueCountFrequency (%)
0 36493
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

genres
Categorical

Distinct4068
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Drama
4998 
Comedy
3621 
Documentary
 
2713
sin datos
 
2384
Drama, Romance
 
1301
Other values (4063)
30362 

Length

Max length84
Median length68
Mean length16.07477
Min length3

Characters and Unicode

Total characters729457
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2367 ?
Unique (%)5.2%

Sample

1st rowAnimation, Comedy, Family
2nd rowAdventure, Fantasy, Family
3rd rowRomance, Comedy
4th rowComedy, Drama, Romance
5th rowComedy

Common Values

ValueCountFrequency (%)
Drama 4998
 
11.0%
Comedy 3621
 
8.0%
Documentary 2713
 
6.0%
sin datos 2384
 
5.3%
Drama, Romance 1301
 
2.9%
Comedy, Drama 1135
 
2.5%
Horror 974
 
2.1%
Comedy, Romance 930
 
2.0%
Comedy, Drama, Romance 593
 
1.3%
Drama, Comedy 532
 
1.2%
Other values (4058) 26198
57.7%

Length

2023-07-11T14:35:38.740296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama 20255
20.3%
comedy 13181
13.2%
thriller 7619
 
7.6%
romance 6733
 
6.8%
action 6592
 
6.6%
horror 4670
 
4.7%
crime 4305
 
4.3%
documentary 3921
 
3.9%
adventure 3494
 
3.5%
science 3042
 
3.1%
Other values (38) 25827
25.9%

Most occurring characters

ValueCountFrequency (%)
r 69082
 
9.5%
a 64206
 
8.8%
e 55786
 
7.6%
54260
 
7.4%
m 53101
 
7.3%
o 50925
 
7.0%
, 48053
 
6.6%
i 42054
 
5.8%
n 38060
 
5.2%
t 28594
 
3.9%
Other values (30) 225336
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 531500
72.9%
Uppercase Letter 95644
 
13.1%
Space Separator 54260
 
7.4%
Other Punctuation 48053
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 69082
13.0%
a 64206
12.1%
e 55786
10.5%
m 53101
10.0%
o 50925
9.6%
i 42054
7.9%
n 38060
7.2%
t 28594
 
5.4%
y 28510
 
5.4%
c 27977
 
5.3%
Other values (12) 73205
13.8%
Uppercase Letter
ValueCountFrequency (%)
D 24176
25.3%
C 17489
18.3%
A 12020
12.6%
F 9746
10.2%
T 8389
 
8.8%
R 6735
 
7.0%
H 6068
 
6.3%
M 4830
 
5.0%
S 3046
 
3.2%
W 2365
 
2.5%
Other values (6) 780
 
0.8%
Space Separator
ValueCountFrequency (%)
54260
100.0%
Other Punctuation
ValueCountFrequency (%)
, 48053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 627144
86.0%
Common 102313
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 69082
11.0%
a 64206
 
10.2%
e 55786
 
8.9%
m 53101
 
8.5%
o 50925
 
8.1%
i 42054
 
6.7%
n 38060
 
6.1%
t 28594
 
4.6%
y 28510
 
4.5%
c 27977
 
4.5%
Other values (28) 168849
26.9%
Common
ValueCountFrequency (%)
54260
53.0%
, 48053
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 69082
 
9.5%
a 64206
 
8.8%
e 55786
 
7.6%
54260
 
7.4%
m 53101
 
7.3%
o 50925
 
7.0%
, 48053
 
6.6%
i 42054
 
5.8%
n 38060
 
5.2%
t 28594
 
3.9%
Other values (30) 225336
30.9%

id
Real number (ℝ)

Distinct45347
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108019.96
Minimum1
Maximum469172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-07-11T14:35:39.009050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5333.9
Q126380.5
median59853
Q3156472
95-th percentile357170.8
Maximum469172
Range469171
Interquartile range (IQR)130091.5

Descriptive statistics

Standard deviation112168.11
Coefficient of variation (CV)1.0384017
Kurtosis0.55969805
Mean108019.96
Median Absolute Deviation (MAD)44421
Skewness1.2831253
Sum4.9018379 × 109
Variance1.2581685 × 1010
MonotonicityNot monotonic
2023-07-11T14:35:39.241382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141971 3
 
< 0.1%
265189 2
 
< 0.1%
119916 2
 
< 0.1%
152795 2
 
< 0.1%
84198 2
 
< 0.1%
159849 2
 
< 0.1%
42495 2
 
< 0.1%
110428 2
 
< 0.1%
97995 2
 
< 0.1%
132641 2
 
< 0.1%
Other values (45337) 45358
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 2
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
469172 1
< 0.1%
468707 1
< 0.1%
468343 1
< 0.1%
467731 1
< 0.1%
465044 1
< 0.1%
464819 1
< 0.1%
464207 1
< 0.1%
464111 1
< 0.1%
463906 1
< 0.1%
463800 1
< 0.1%

original_language
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct93
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
en
32202 
fr
 
2437
it
 
1528
ja
 
1349
de
 
1078
Other values (88)
6785 

Length

Max length9
Median length2
Mean length2.0018511
Min length2

Characters and Unicode

Total characters90842
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 32202
71.0%
fr 2437
 
5.4%
it 1528
 
3.4%
ja 1349
 
3.0%
de 1078
 
2.4%
es 992
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (83) 3611
 
8.0%

Length

2023-07-11T14:35:39.491509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 32202
70.9%
fr 2437
 
5.4%
it 1528
 
3.4%
ja 1349
 
3.0%
de 1078
 
2.4%
es 992
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (84) 3622
 
8.0%

Most occurring characters

ValueCountFrequency (%)
e 34527
38.0%
n 32921
36.2%
r 3630
 
4.0%
f 2835
 
3.1%
i 2399
 
2.6%
t 2261
 
2.5%
a 1850
 
2.0%
s 1674
 
1.8%
j 1350
 
1.5%
d 1334
 
1.5%
Other values (24) 6061
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90818
> 99.9%
Space Separator 11
 
< 0.1%
Decimal Number 10
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34527
38.0%
n 32921
36.2%
r 3630
 
4.0%
f 2835
 
3.1%
i 2399
 
2.6%
t 2261
 
2.5%
a 1850
 
2.0%
s 1674
 
1.8%
j 1350
 
1.5%
d 1334
 
1.5%
Other values (16) 6037
 
6.6%
Decimal Number
ValueCountFrequency (%)
0 4
40.0%
8 2
20.0%
2 1
 
10.0%
6 1
 
10.0%
1 1
 
10.0%
4 1
 
10.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90818
> 99.9%
Common 24
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34527
38.0%
n 32921
36.2%
r 3630
 
4.0%
f 2835
 
3.1%
i 2399
 
2.6%
t 2261
 
2.5%
a 1850
 
2.0%
s 1674
 
1.8%
j 1350
 
1.5%
d 1334
 
1.5%
Other values (16) 6037
 
6.6%
Common
ValueCountFrequency (%)
11
45.8%
0 4
 
16.7%
. 3
 
12.5%
8 2
 
8.3%
2 1
 
4.2%
6 1
 
4.2%
1 1
 
4.2%
4 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34527
38.0%
n 32921
36.2%
r 3630
 
4.0%
f 2835
 
3.1%
i 2399
 
2.6%
t 2261
 
2.5%
a 1850
 
2.0%
s 1674
 
1.8%
j 1350
 
1.5%
d 1334
 
1.5%
Other values (24) 6061
 
6.7%

overview
Categorical

Distinct44234
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
sin datos
 
941
No overview found.
 
133
No Overview
 
7
 
5
Released
 
3
Other values (44229)
44290 

Length

Max length1000
Median length790
Mean length316.75881
Min length1

Characters and Unicode

Total characters14374198
Distinct characters429
Distinct categories25 ?
Distinct scripts13 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44173 ?
Unique (%)97.3%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.

Common Values

ValueCountFrequency (%)
sin datos 941
 
2.1%
No overview found. 133
 
0.3%
No Overview 7
 
< 0.1%
5
 
< 0.1%
Released 3
 
< 0.1%
Recovering from a nail gun shot to the head and 13 months of coma, doctor Pekka Valinta starts to unravel the mystery of his past, still suffering from total amnesia. 3
 
< 0.1%
King Lear, old and tired, divides his kingdom among his daughters, giving great importance to their protestations of love for him. When Cordelia, youngest and most honest, refuses to idly flatter the old man in return for favor, he banishes her and turns for support to his remaining daughters. But Goneril and Regan have no love for him and instead plot to take all his power from him. In a parallel, Lear's loyal courtier Gloucester favors his illegitimate son Edmund after being told lies about his faithful son Edgar. Madness and tragedy befall both ill-starred fathers. 3
 
< 0.1%
No movie overview available. 3
 
< 0.1%
Adaptation of the Jane Austen novel. 3
 
< 0.1%
A few funny little novels about different aspects of life. 3
 
< 0.1%
Other values (44224) 44275
97.6%

Length

2023-07-11T14:35:39.770977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 138082
 
5.6%
a 98889
 
4.0%
and 75259
 
3.1%
to 73321
 
3.0%
of 69574
 
2.8%
in 48143
 
2.0%
is 36500
 
1.5%
his 36165
 
1.5%
with 23902
 
1.0%
her 21484
 
0.9%
Other values (97092) 1829274
74.6%

Most occurring characters

ValueCountFrequency (%)
2407291
16.7%
e 1363796
 
9.5%
a 941446
 
6.5%
t 935707
 
6.5%
i 852455
 
5.9%
o 830814
 
5.8%
n 823542
 
5.7%
s 769736
 
5.4%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4104327
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11157610
77.6%
Space Separator 2407329
 
16.7%
Uppercase Letter 390965
 
2.7%
Other Punctuation 312824
 
2.2%
Decimal Number 42223
 
0.3%
Dash Punctuation 36767
 
0.3%
Close Punctuation 10100
 
0.1%
Open Punctuation 10077
 
0.1%
Final Punctuation 4556
 
< 0.1%
Initial Punctuation 882
 
< 0.1%
Other values (15) 865
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1363796
12.2%
a 941446
 
8.4%
t 935707
 
8.4%
i 852455
 
7.6%
o 830814
 
7.4%
n 823542
 
7.4%
s 769736
 
6.9%
r 744274
 
6.7%
h 600810
 
5.4%
l 478816
 
4.3%
Other values (142) 2816214
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 42751
 
10.9%
T 35968
 
9.2%
S 31126
 
8.0%
M 23954
 
6.1%
B 23699
 
6.1%
C 22803
 
5.8%
H 19429
 
5.0%
W 18652
 
4.8%
I 16798
 
4.3%
D 16311
 
4.2%
Other values (77) 139474
35.7%
Other Letter
ValueCountFrequency (%)
6
 
4.8%
6
 
4.8%
5
 
4.0%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
2
 
1.6%
م 2
 
1.6%
Other values (76) 88
70.4%
Other Punctuation
ValueCountFrequency (%)
, 133443
42.7%
. 124794
39.9%
' 31121
 
9.9%
" 11661
 
3.7%
: 3299
 
1.1%
? 2759
 
0.9%
; 2493
 
0.8%
! 1543
 
0.5%
/ 765
 
0.2%
& 453
 
0.1%
Other values (12) 493
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 4
12.1%
ి 4
12.1%
3
9.1%
3
9.1%
3
9.1%
̈ 3
9.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
Other values (4) 5
15.2%
Decimal Number
ValueCountFrequency (%)
1 9748
23.1%
0 8265
19.6%
9 6405
15.2%
2 4251
10.1%
5 2440
 
5.8%
8 2379
 
5.6%
3 2342
 
5.5%
4 2176
 
5.2%
7 2131
 
5.0%
6 2086
 
4.9%
Spacing Mark
ValueCountFrequency (%)
11
40.7%
4
 
14.8%
3
 
11.1%
3
 
11.1%
ि 2
 
7.4%
2
 
7.4%
1
 
3.7%
ி 1
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 35244
95.9%
881
 
2.4%
633
 
1.7%
5
 
< 0.1%
4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 45
70.3%
14
 
21.9%
¦ 2
 
3.1%
° 2
 
3.1%
1
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 20
50.0%
+ 11
27.5%
= 6
 
15.0%
| 2
 
5.0%
1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 10024
99.5%
[ 50
 
0.5%
{ 2
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 317
96.4%
£ 10
 
3.0%
1
 
0.3%
1
 
0.3%
Space Separator
ValueCountFrequency (%)
2407291
> 99.9%
  36
 
< 0.1%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10048
99.5%
] 50
 
0.5%
} 2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
3847
84.4%
690
 
15.1%
» 19
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
672
76.2%
192
 
21.8%
« 18
 
2.0%
Control
ValueCountFrequency (%)
106
96.4%
’ 3
 
2.7%
 1
 
0.9%
Modifier Symbol
ValueCountFrequency (%)
´ 25
65.8%
` 12
31.6%
¯ 1
 
2.6%
Format
ValueCountFrequency (%)
31
60.8%
­ 20
39.2%
Other Number
ValueCountFrequency (%)
½ 8
50.0%
¹ 8
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Line Separator
ValueCountFrequency (%)
7
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Paragraph Separator
ValueCountFrequency (%)
2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʼ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11543343
80.3%
Common 2825436
 
19.7%
Cyrillic 4587
 
< 0.1%
Greek 648
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Han 10
 
< 0.1%
Hangul 9
 
< 0.1%
Other values (3) 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1363796
11.8%
a 941446
 
8.2%
t 935707
 
8.1%
i 852455
 
7.4%
o 830814
 
7.2%
n 823542
 
7.1%
s 769736
 
6.7%
r 744274
 
6.4%
h 600810
 
5.2%
l 478816
 
4.1%
Other values (132) 3201947
27.7%
Common
ValueCountFrequency (%)
2407291
85.2%
, 133443
 
4.7%
. 124794
 
4.4%
- 35244
 
1.2%
' 31121
 
1.1%
" 11661
 
0.4%
) 10048
 
0.4%
( 10024
 
0.4%
1 9748
 
0.3%
0 8265
 
0.3%
Other values (71) 43797
 
1.6%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Greek
ValueCountFrequency (%)
α 60
 
9.3%
ο 55
 
8.5%
τ 43
 
6.6%
ι 36
 
5.6%
η 36
 
5.6%
ν 34
 
5.2%
ε 31
 
4.8%
ρ 31
 
4.8%
π 30
 
4.6%
ς 30
 
4.6%
Other values (33) 262
40.4%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Han
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Inherited
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14356200
99.9%
Punctuation 7270
 
0.1%
None 5930
 
< 0.1%
Cyrillic 4587
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Letterlike Symbols 14
 
< 0.1%
CJK 10
 
< 0.1%
Other values (11) 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2407291
16.8%
e 1363796
 
9.5%
a 941446
 
6.6%
t 935707
 
6.5%
i 852455
 
5.9%
o 830814
 
5.8%
n 823542
 
5.7%
s 769736
 
5.4%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (82) 4086329
28.5%
Punctuation
ValueCountFrequency (%)
3847
52.9%
881
 
12.1%
690
 
9.5%
672
 
9.2%
633
 
8.7%
303
 
4.2%
192
 
2.6%
31
 
0.4%
7
 
0.1%
5
 
0.1%
Other values (4) 9
 
0.1%
None
ValueCountFrequency (%)
é 1552
26.2%
ä 294
 
5.0%
á 293
 
4.9%
ö 250
 
4.2%
í 243
 
4.1%
è 209
 
3.5%
ü 178
 
3.0%
ı 165
 
2.8%
ó 164
 
2.8%
ç 158
 
2.7%
Other values (141) 2424
40.9%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Letterlike Symbols
ValueCountFrequency (%)
14
100.0%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Alphabetic PF
ValueCountFrequency (%)
4
100.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Diacriticals
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ 2
100.0%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
CJK
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Specials
ValueCountFrequency (%)
1
100.0%

popularity
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.7 MiB

production_companies
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.7 MiB

production_countries
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.7 MiB
Distinct17334
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2023-07-11T14:35:40.011053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:40.271052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11229357
Minimum0
Maximum2.7879651 × 109
Zeros37972
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:40.490110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48018459
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64387893
Coefficient of variation (CV)5.7338897
Kurtosis237.09288
Mean11229357
Median Absolute Deviation (MAD)0
Skewness12.255124
Sum5.0957698 × 1011
Variance4.1458008 × 1015
MonotonicityNot monotonic
2023-07-11T14:35:40.707898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37972
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7262
 
16.0%
ValueCountFrequency (%)
0 37972
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

Distinct353
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.664889
Minimum0
Maximum1256
Zeros1784
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:40.929233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.863558
Coefficient of variation (CV)0.4149213
Kurtosis88.727685
Mean93.664889
Median Absolute Deviation (MAD)11
Skewness4.2501696
Sum4250419
Variance1510.3761
MonotonicityNot monotonic
2023-07-11T14:35:41.205032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2549
 
5.6%
0 1784
 
3.9%
100 1470
 
3.2%
95 1410
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1079
 
2.4%
94 1062
 
2.3%
91 1055
 
2.3%
88 1030
 
2.3%
Other values (343) 31622
69.7%
ValueCountFrequency (%)
0 1784
3.9%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 50
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

spoken_languages
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct1842
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
English
22380 
sin datos
3894 
Français
 
1852
日本語
 
1289
Italiano
 
1217
Other values (1837)
14747 

Length

Max length171
Median length7
Mean length9.3637145
Min length2

Characters and Unicode

Total characters424916
Distinct characters171
Distinct categories8 ?
Distinct scripts15 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1293 ?
Unique (%)2.8%

Sample

1st rowEnglish
2nd rowEnglish, Français
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 22380
49.3%
sin datos 3894
 
8.6%
Français 1852
 
4.1%
日本語 1289
 
2.8%
Italiano 1217
 
2.7%
Español 901
 
2.0%
Pусский 807
 
1.8%
Deutsch 761
 
1.7%
English, Français 681
 
1.5%
English, Español 572
 
1.3%
Other values (1832) 11025
24.3%

Length

2023-07-11T14:35:41.470607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 28729
46.2%
français 4194
 
6.7%
sin 3894
 
6.3%
datos 3894
 
6.3%
deutsch 2624
 
4.2%
español 2412
 
3.9%
italiano 2366
 
3.8%
日本語 1758
 
2.8%
pусский 1562
 
2.5%
普通话 790
 
1.3%
Other values (71) 9935
 
16.0%

Most occurring characters

ValueCountFrequency (%)
s 50058
11.8%
n 41356
 
9.7%
i 41003
 
9.6%
l 34631
 
8.2%
h 31459
 
7.4%
E 31198
 
7.3%
g 30413
 
7.2%
a 22840
 
5.4%
16973
 
4.0%
, 11666
 
2.7%
Other values (161) 113319
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 323180
76.1%
Uppercase Letter 46428
 
10.9%
Other Letter 22191
 
5.2%
Space Separator 16973
 
4.0%
Other Punctuation 12731
 
3.0%
Spacing Mark 1838
 
0.4%
Nonspacing Mark 1549
 
0.4%
Control 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 50058
15.5%
n 41356
12.8%
i 41003
12.7%
l 34631
10.7%
h 31459
9.7%
g 30413
9.4%
a 22840
7.1%
o 10947
 
3.4%
t 9871
 
3.1%
r 6128
 
1.9%
Other values (63) 44474
13.8%
Other Letter
ValueCountFrequency (%)
1758
 
7.9%
1758
 
7.9%
1758
 
7.9%
1263
 
5.7%
946
 
4.3%
790
 
3.6%
790
 
3.6%
707
 
3.2%
707
 
3.2%
707
 
3.2%
Other values (46) 11007
49.6%
Uppercase Letter
ValueCountFrequency (%)
E 31198
67.2%
F 4196
 
9.0%
D 2926
 
6.3%
P 2677
 
5.8%
I 2366
 
5.1%
N 829
 
1.8%
L 505
 
1.1%
M 362
 
0.8%
T 308
 
0.7%
Č 284
 
0.6%
Other values (13) 777
 
1.7%
Spacing Mark
ValueCountFrequency (%)
ि 707
38.5%
707
38.5%
136
 
7.4%
ி 111
 
6.0%
94
 
5.1%
47
 
2.6%
18
 
1.0%
18
 
1.0%
Nonspacing Mark
ValueCountFrequency (%)
707
45.6%
ִ 430
27.8%
ְ 215
 
13.9%
111
 
7.2%
68
 
4.4%
18
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 11666
91.6%
/ 1015
 
8.0%
? 50
 
0.4%
Space Separator
ValueCountFrequency (%)
16973
100.0%
Control
ValueCountFrequency (%)
š 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 357219
84.1%
Common 29730
 
7.0%
Han 10482
 
2.5%
Cyrillic 10454
 
2.5%
Devanagari 4242
 
1.0%
Arabic 3344
 
0.8%
Hangul 3252
 
0.8%
Hebrew 1720
 
0.4%
Greek 1704
 
0.4%
Thai 1232
 
0.3%
Other values (5) 1537
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 50058
14.0%
n 41356
11.6%
i 41003
11.5%
l 34631
9.7%
h 31459
8.8%
E 31198
8.7%
g 30413
8.5%
a 22840
6.4%
o 10947
 
3.1%
t 9871
 
2.8%
Other values (50) 53443
15.0%
Cyrillic
ValueCountFrequency (%)
с 3211
30.7%
к 1734
16.6%
и 1679
16.1%
й 1615
15.4%
у 1564
15.0%
а 113
 
1.1%
р 87
 
0.8%
У 53
 
0.5%
ї 53
 
0.5%
н 53
 
0.5%
Other values (12) 292
 
2.8%
Arabic
ValueCountFrequency (%)
ا 537
16.1%
ر 537
16.1%
ل 341
10.2%
ع 341
10.2%
ب 341
10.2%
ي 341
10.2%
ة 341
10.2%
ی 141
 
4.2%
ف 141
 
4.2%
س 141
 
4.2%
Other values (5) 142
 
4.2%
Han
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
473
 
4.5%
473
 
4.5%
广 473
 
4.5%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
ת 215
12.5%
י 215
12.5%
ר 215
12.5%
ְ 215
12.5%
ב 215
12.5%
ע 215
12.5%
Greek
ValueCountFrequency (%)
λ 426
25.0%
ά 213
12.5%
κ 213
12.5%
ι 213
12.5%
ν 213
12.5%
ε 213
12.5%
η 213
12.5%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Devanagari
ValueCountFrequency (%)
ि 707
16.7%
707
16.7%
707
16.7%
707
16.7%
707
16.7%
707
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Thai
ValueCountFrequency (%)
352
28.6%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
Common
ValueCountFrequency (%)
16973
57.1%
, 11666
39.2%
/ 1015
 
3.4%
? 50
 
0.2%
š 26
 
0.1%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378093
89.0%
CJK 10482
 
2.5%
Cyrillic 10454
 
2.5%
None 10434
 
2.5%
Devanagari 4242
 
1.0%
Arabic 3344
 
0.8%
Hangul 3252
 
0.8%
Hebrew 1720
 
0.4%
Thai 1232
 
0.3%
Tamil 555
 
0.1%
Other values (6) 1108
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 50058
13.2%
n 41356
10.9%
i 41003
10.8%
l 34631
9.2%
h 31459
8.3%
E 31198
8.3%
g 30413
8.0%
a 22840
 
6.0%
16973
 
4.5%
, 11666
 
3.1%
Other values (38) 66496
17.6%
None
ValueCountFrequency (%)
ç 4441
42.6%
ñ 2412
23.1%
ê 591
 
5.7%
λ 426
 
4.1%
ý 284
 
2.7%
Č 284
 
2.7%
ü 247
 
2.4%
ά 213
 
2.0%
κ 213
 
2.0%
ι 213
 
2.0%
Other values (11) 1110
 
10.6%
Cyrillic
ValueCountFrequency (%)
с 3211
30.7%
к 1734
16.6%
и 1679
16.1%
й 1615
15.4%
у 1564
15.0%
а 113
 
1.1%
р 87
 
0.8%
У 53
 
0.5%
ї 53
 
0.5%
н 53
 
0.5%
Other values (12) 292
 
2.8%
CJK
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
473
 
4.5%
473
 
4.5%
广 473
 
4.5%
Devanagari
ValueCountFrequency (%)
ि 707
16.7%
707
16.7%
707
16.7%
707
16.7%
707
16.7%
707
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Arabic
ValueCountFrequency (%)
ا 537
16.1%
ر 537
16.1%
ل 341
10.2%
ع 341
10.2%
ب 341
10.2%
ي 341
10.2%
ة 341
10.2%
ی 141
 
4.2%
ف 141
 
4.2%
س 141
 
4.2%
Other values (5) 142
 
4.2%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
ת 215
12.5%
י 215
12.5%
ר 215
12.5%
ְ 215
12.5%
ב 215
12.5%
ע 215
12.5%
Thai
ValueCountFrequency (%)
352
28.6%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%
Latin Ext Additional
ValueCountFrequency (%)
ế 61
50.0%
61
50.0%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
IPA Ext
ValueCountFrequency (%)
ə 4
100.0%

status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Released
44936 
Rumored
 
230
Post Production
 
97
sin datos
 
83
In Production
 
19
Other values (2)
 
14

Length

Max length15
Median length8
Mean length8.0135305
Min length7

Characters and Unicode

Total characters363646
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44936
99.0%
Rumored 230
 
0.5%
Post Production 97
 
0.2%
sin datos 83
 
0.2%
In Production 19
 
< 0.1%
Planned 13
 
< 0.1%
Canceled 1
 
< 0.1%

Length

2023-07-11T14:35:42.009034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T14:35:42.274337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
released 44936
98.6%
rumored 230
 
0.5%
production 116
 
0.3%
post 97
 
0.2%
sin 83
 
0.2%
datos 83
 
0.2%
in 19
 
< 0.1%
planned 13
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135053
37.1%
d 45379
 
12.5%
s 45199
 
12.4%
R 45166
 
12.4%
a 45033
 
12.4%
l 44950
 
12.4%
o 642
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
t 296
 
0.1%
Other values (8) 1236
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 318035
87.5%
Uppercase Letter 45412
 
12.5%
Space Separator 199
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 135053
42.5%
d 45379
 
14.3%
s 45199
 
14.2%
a 45033
 
14.2%
l 44950
 
14.1%
o 642
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
t 296
 
0.1%
n 245
 
0.1%
Other values (3) 546
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
R 45166
99.5%
P 226
 
0.5%
I 19
 
< 0.1%
C 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363447
99.9%
Common 199
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 135053
37.2%
d 45379
 
12.5%
s 45199
 
12.4%
R 45166
 
12.4%
a 45033
 
12.4%
l 44950
 
12.4%
o 642
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
t 296
 
0.1%
Other values (7) 1037
 
0.3%
Common
ValueCountFrequency (%)
199
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 135053
37.1%
d 45379
 
12.5%
s 45199
 
12.4%
R 45166
 
12.4%
a 45033
 
12.4%
l 44950
 
12.4%
o 642
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
t 296
 
0.1%
Other values (8) 1236
 
0.3%

tagline
Categorical

Distinct20270
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
sin datos
24981 
Based on a true story.
 
7
Trust no one.
 
4
Be careful what you wish for.
 
4
-
 
4
Other values (20265)
20379 

Length

Max length297
Median length9
Mean length26.080808
Min length1

Characters and Unicode

Total characters1183521
Distinct characters170
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20163 ?
Unique (%)44.4%

Sample

1st rowsin datos
2nd rowRoll the dice and unleash the excitement!
3rd rowStill Yelling. Still Fighting. Still Ready for Love.
4th rowFriends are the people who let you be yourself... and never let you forget it.
5th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!

Common Values

ValueCountFrequency (%)
sin datos 24981
55.0%
Based on a true story. 7
 
< 0.1%
Trust no one. 4
 
< 0.1%
Be careful what you wish for. 4
 
< 0.1%
- 4
 
< 0.1%
How far would you go? 3
 
< 0.1%
Drama 3
 
< 0.1%
Classic Albums 3
 
< 0.1%
There are two sides to every love story. 3
 
< 0.1%
There is no turning back 3
 
< 0.1%
Other values (20260) 20364
44.9%

Length

2023-07-11T14:35:42.575144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sin 25008
 
11.2%
datos 24981
 
11.2%
the 10998
 
4.9%
a 6815
 
3.0%
of 4404
 
2.0%
to 3584
 
1.6%
is 2796
 
1.2%
in 2693
 
1.2%
and 2682
 
1.2%
you 2389
 
1.1%
Other values (15101) 137548
61.4%

Most occurring characters

ValueCountFrequency (%)
178667
15.1%
e 94412
 
8.0%
s 92322
 
7.8%
t 82248
 
6.9%
o 81547
 
6.9%
a 76454
 
6.5%
n 72479
 
6.1%
i 71017
 
6.0%
d 48472
 
4.1%
r 44992
 
3.8%
Other values (160) 340911
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 880327
74.4%
Space Separator 178667
 
15.1%
Uppercase Letter 74991
 
6.3%
Other Punctuation 44585
 
3.8%
Decimal Number 2687
 
0.2%
Dash Punctuation 1944
 
0.2%
Final Punctuation 98
 
< 0.1%
Open Punctuation 56
 
< 0.1%
Close Punctuation 55
 
< 0.1%
Currency Symbol 37
 
< 0.1%
Other values (7) 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 94412
10.7%
s 92322
10.5%
t 82248
9.3%
o 81547
9.3%
a 76454
8.7%
n 72479
8.2%
i 71017
 
8.1%
d 48472
 
5.5%
r 44992
 
5.1%
h 37172
 
4.2%
Other values (43) 179212
20.4%
Other Letter
ValueCountFrequency (%)
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (24) 24
70.6%
Uppercase Letter
ValueCountFrequency (%)
T 10009
 
13.3%
A 6874
 
9.2%
S 5652
 
7.5%
H 4402
 
5.9%
I 4387
 
5.9%
E 4306
 
5.7%
W 3681
 
4.9%
O 3477
 
4.6%
N 3195
 
4.3%
L 3194
 
4.3%
Other values (20) 25814
34.4%
Other Punctuation
ValueCountFrequency (%)
. 26647
59.8%
! 5784
 
13.0%
' 5674
 
12.7%
, 4226
 
9.5%
? 1161
 
2.6%
" 582
 
1.3%
148
 
0.3%
: 138
 
0.3%
& 83
 
0.2%
* 42
 
0.1%
Other values (7) 100
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 802
29.8%
1 516
19.2%
2 299
 
11.1%
3 208
 
7.7%
9 208
 
7.7%
5 168
 
6.3%
4 140
 
5.2%
7 121
 
4.5%
6 121
 
4.5%
8 104
 
3.9%
Math Symbol
ValueCountFrequency (%)
+ 5
35.7%
= 5
35.7%
| 2
 
14.3%
~ 1
 
7.1%
1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 1927
99.1%
9
 
0.5%
8
 
0.4%
Final Punctuation
ValueCountFrequency (%)
82
83.7%
15
 
15.3%
» 1
 
1.0%
Initial Punctuation
ValueCountFrequency (%)
14
73.7%
4
 
21.1%
« 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 49
87.5%
[ 7
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 48
87.3%
] 7
 
12.7%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Modifier Letter
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Space Separator
ValueCountFrequency (%)
178667
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 37
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 955318
80.7%
Common 228168
 
19.3%
Han 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 94412
 
9.9%
s 92322
 
9.7%
t 82248
 
8.6%
o 81547
 
8.5%
a 76454
 
8.0%
n 72479
 
7.6%
i 71017
 
7.4%
d 48472
 
5.1%
r 44992
 
4.7%
h 37172
 
3.9%
Other values (73) 254203
26.6%
Common
ValueCountFrequency (%)
178667
78.3%
. 26647
 
11.7%
! 5784
 
2.5%
' 5674
 
2.5%
, 4226
 
1.9%
- 1927
 
0.8%
? 1161
 
0.5%
0 802
 
0.4%
" 582
 
0.3%
1 516
 
0.2%
Other values (42) 2182
 
1.0%
Han
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1183091
> 99.9%
Punctuation 280
 
< 0.1%
None 110
 
< 0.1%
CJK 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%
IPA Ext 2
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
178667
15.1%
e 94412
 
8.0%
s 92322
 
7.8%
t 82248
 
7.0%
o 81547
 
6.9%
a 76454
 
6.5%
n 72479
 
6.1%
i 71017
 
6.0%
d 48472
 
4.1%
r 44992
 
3.8%
Other values (78) 340481
28.8%
Punctuation
ValueCountFrequency (%)
148
52.9%
82
29.3%
15
 
5.4%
14
 
5.0%
9
 
3.2%
8
 
2.9%
4
 
1.4%
None
ValueCountFrequency (%)
é 18
16.4%
ä 16
14.5%
ö 8
 
7.3%
á 6
 
5.5%
ó 6
 
5.5%
ü 5
 
4.5%
í 5
 
4.5%
ı 5
 
4.5%
· 4
 
3.6%
ć 3
 
2.7%
Other values (26) 34
30.9%
IPA Ext
ValueCountFrequency (%)
ə 2
100.0%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
CJK
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Modifier Letters
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct42197
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Cinderella
 
11
Hamlet
 
9
Alice in Wonderland
 
9
Les Misérables
 
8
Beauty and the Beast
 
8
Other values (42192)
45334 

Length

Max length105
Median length79
Mean length16.701272
Min length1

Characters and Unicode

Total characters757887
Distinct characters287
Distinct categories17 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39869 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II

Common Values

ValueCountFrequency (%)
Cinderella 11
 
< 0.1%
Hamlet 9
 
< 0.1%
Alice in Wonderland 9
 
< 0.1%
Les Misérables 8
 
< 0.1%
Beauty and the Beast 8
 
< 0.1%
Treasure Island 7
 
< 0.1%
The Three Musketeers 7
 
< 0.1%
Blackout 7
 
< 0.1%
A Christmas Carol 7
 
< 0.1%
Aftermath 6
 
< 0.1%
Other values (42187) 45300
99.8%

Length

2023-07-11T14:35:42.956234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 14555
 
10.7%
of 4930
 
3.6%
a 2241
 
1.6%
in 1693
 
1.2%
and 1631
 
1.2%
to 1054
 
0.8%
757
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 601
 
0.4%
Other values (24354) 107396
78.9%

Most occurring characters

ValueCountFrequency (%)
90830
 
12.0%
e 76251
 
10.1%
a 48943
 
6.5%
o 45674
 
6.0%
n 40820
 
5.4%
r 40018
 
5.3%
i 39767
 
5.2%
t 36725
 
4.8%
s 29525
 
3.9%
h 28516
 
3.8%
Other values (277) 280818
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534158
70.5%
Uppercase Letter 117265
 
15.5%
Space Separator 90830
 
12.0%
Other Punctuation 10489
 
1.4%
Decimal Number 3850
 
0.5%
Dash Punctuation 981
 
0.1%
Close Punctuation 87
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Final Punctuation 38
 
< 0.1%
Other Letter 25
 
< 0.1%
Other values (7) 79
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 76251
14.3%
a 48943
9.2%
o 45674
 
8.6%
n 40820
 
7.6%
r 40018
 
7.5%
i 39767
 
7.4%
t 36725
 
6.9%
s 29525
 
5.5%
h 28516
 
5.3%
l 25924
 
4.9%
Other values (121) 121995
22.8%
Uppercase Letter
ValueCountFrequency (%)
T 16019
13.7%
S 10336
 
8.8%
M 8031
 
6.8%
B 7659
 
6.5%
C 7165
 
6.1%
A 6785
 
5.8%
D 6335
 
5.4%
L 5872
 
5.0%
H 5170
 
4.4%
W 5166
 
4.4%
Other values (65) 38727
33.0%
Other Letter
ValueCountFrequency (%)
چ 2
 
8.0%
ه 2
 
8.0%
ی 2
 
8.0%
ک 2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
ª 1
 
4.0%
Other values (11) 11
44.0%
Other Punctuation
ValueCountFrequency (%)
: 3717
35.4%
' 2505
23.9%
. 1603
15.3%
, 1134
 
10.8%
! 647
 
6.2%
& 458
 
4.4%
? 269
 
2.6%
/ 79
 
0.8%
* 19
 
0.2%
# 13
 
0.1%
Other values (8) 45
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 861
22.4%
1 697
18.1%
0 616
16.0%
3 482
12.5%
9 230
 
6.0%
4 229
 
5.9%
5 225
 
5.8%
7 193
 
5.0%
8 161
 
4.2%
6 156
 
4.1%
Math Symbol
ValueCountFrequency (%)
+ 17
70.8%
× 3
 
12.5%
1
 
4.2%
= 1
 
4.2%
1
 
4.2%
1
 
4.2%
Other Number
ValueCountFrequency (%)
½ 12
63.2%
² 3
 
15.8%
³ 2
 
10.5%
1
 
5.3%
1
 
5.3%
Other Symbol
ValueCountFrequency (%)
° 3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
$ 18
85.7%
¢ 2
 
9.5%
£ 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 966
98.5%
15
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 82
94.3%
] 5
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 80
94.1%
[ 5
 
5.9%
Final Punctuation
ValueCountFrequency (%)
37
97.4%
1
 
2.6%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
90830
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Format
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 650908
85.9%
Common 106439
 
14.0%
Cyrillic 346
 
< 0.1%
Greek 170
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
Han 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 76251
 
11.7%
a 48943
 
7.5%
o 45674
 
7.0%
n 40820
 
6.3%
r 40018
 
6.1%
i 39767
 
6.1%
t 36725
 
5.6%
s 29525
 
4.5%
h 28516
 
4.4%
l 25924
 
4.0%
Other values (107) 238745
36.7%
Common
ValueCountFrequency (%)
90830
85.3%
: 3717
 
3.5%
' 2505
 
2.4%
. 1603
 
1.5%
, 1134
 
1.1%
- 966
 
0.9%
2 861
 
0.8%
1 697
 
0.7%
! 647
 
0.6%
0 616
 
0.6%
Other values (50) 2863
 
2.7%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
в 14
 
4.0%
Other values (38) 124
35.8%
Greek
ValueCountFrequency (%)
α 20
 
11.8%
ι 14
 
8.2%
ο 14
 
8.2%
τ 9
 
5.3%
ά 8
 
4.7%
λ 8
 
4.7%
ρ 8
 
4.7%
ν 7
 
4.1%
ε 6
 
3.5%
ς 6
 
3.5%
Other values (32) 70
41.2%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756322
99.8%
None 1124
 
0.1%
Cyrillic 346
 
< 0.1%
Punctuation 62
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
CJK 5
 
< 0.1%
Misc Symbols 3
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Math Operators 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90830
 
12.0%
e 76251
 
10.1%
a 48943
 
6.5%
o 45674
 
6.0%
n 40820
 
5.4%
r 40018
 
5.3%
i 39767
 
5.3%
t 36725
 
4.9%
s 29525
 
3.9%
h 28516
 
3.8%
Other values (76) 279253
36.9%
None
ValueCountFrequency (%)
é 218
19.4%
ä 127
 
11.3%
ö 55
 
4.9%
è 53
 
4.7%
ô 44
 
3.9%
ü 39
 
3.5%
ó 37
 
3.3%
á 35
 
3.1%
ı 35
 
3.1%
í 33
 
2.9%
Other values (108) 448
39.9%
Punctuation
ValueCountFrequency (%)
37
59.7%
15
24.2%
5
 
8.1%
2
 
3.2%
1
 
1.6%
1
 
1.6%
1
 
1.6%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
в 14
 
4.0%
Other values (38) 124
35.8%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arrows
ValueCountFrequency (%)
1
100.0%

vote_average
Real number (ℝ)

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6236982
Minimum0
Maximum10
Zeros2950
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:43.273253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.915905
Coefficient of variation (CV)0.34068417
Kurtosis2.5398342
Mean5.6236982
Median Absolute Deviation (MAD)0.9
Skewness-1.5243101
Sum255197.8
Variance3.6706919
MonotonicityNot monotonic
2023-07-11T14:35:43.567073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2950
 
6.5%
6 2462
 
5.4%
5 1998
 
4.4%
7 1883
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27319
60.2%
ValueCountFrequency (%)
0 2950
6.5%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 185
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1820
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.08916
Minimum0
Maximum14075
Zeros2852
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:43.891062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.72745
Coefficient of variation (CV)4.4666292
Kurtosis150.93835
Mean110.08916
Median Absolute Deviation (MAD)8
Skewness10.441119
Sum4995736
Variance241795.89
MonotonicityNot monotonic
2023-07-11T14:35:44.237041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3242
 
7.1%
2 3127
 
6.9%
0 2852
 
6.3%
3 2785
 
6.1%
4 2478
 
5.5%
5 2097
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22928
50.5%
ValueCountFrequency (%)
0 2852
6.3%
1 3242
7.1%
2 3127
6.9%
3 2785
6.1%
4 2478
5.5%
5 2097
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

release_year
Real number (ℝ)

Distinct135
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.8822
Minimum1874
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:44.505038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2020
Range146
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.054986
Coefficient of variation (CV)0.01207651
Kurtosis0.84032964
Mean1991.8822
Median Absolute Deviation (MAD)12
Skewness-1.2249397
Sum90389624
Variance578.64235
MonotonicityNot monotonic
2023-07-11T14:35:44.766231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1975
 
4.4%
2015 1905
 
4.2%
2013 1889
 
4.2%
2012 1723
 
3.8%
2011 1667
 
3.7%
2016 1604
 
3.5%
2009 1586
 
3.5%
2010 1501
 
3.3%
2008 1473
 
3.2%
2007 1320
 
2.9%
Other values (125) 28736
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2018 5
 
< 0.1%
2017 532
 
1.2%
2016 1604
3.5%
2015 1905
4.2%
2014 1975
4.4%
2013 1889
4.2%
2012 1723
3.8%
2011 1667
3.7%
2010 1501
3.3%

return
Real number (ℝ)

SKEWED  ZEROS 

Distinct5226
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.99862
Minimum0
Maximum12396383
Zeros40005
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-11T14:35:45.039176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5316
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74690.825
Coefficient of variation (CV)113.16815
Kurtosis20674.324
Mean659.99862
Median Absolute Deviation (MAD)0
Skewness138.3341
Sum29950077
Variance5.5787194 × 109
MonotonicityNot monotonic
2023-07-11T14:35:45.320813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40005
88.2%
1 20
 
< 0.1%
2 12
 
< 0.1%
4 10
 
< 0.1%
5 8
 
< 0.1%
1.333333333 7
 
< 0.1%
2.5 7
 
< 0.1%
3 7
 
< 0.1%
1.5 6
 
< 0.1%
4.666666667 4
 
< 0.1%
Other values (5216) 5293
 
11.7%
ValueCountFrequency (%)
0 40005
88.2%
5.217391304 × 10-71
 
< 0.1%
7.5 × 10-71
 
< 0.1%
9.375 × 10-71
 
< 0.1%
1.499133126 × 10-61
 
< 0.1%
1.8 × 10-61
 
< 0.1%
1.916666667 × 10-61
 
< 0.1%
3.5 × 10-61
 
< 0.1%
4 × 10-61
 
< 0.1%
5.111111111 × 10-61
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.625 1
< 0.1%
2755584 1
< 0.1%
1018619.283 1
< 0.1%
1000000 1
< 0.1%
26881.72043 1
< 0.1%
12890.38667 1
< 0.1%
5330.33945 1
< 0.1%
4133.333333 1
< 0.1%

name_collection
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct1696
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
sin datos
40891 
The Bowery Boys
 
29
Totò Collection
 
27
James Bond Collection
 
26
Zatôichi: The Blind Swordsman
 
26
Other values (1691)
4380 

Length

Max length54
Median length9
Mean length10.469248
Min length3

Characters and Unicode

Total characters475084
Distinct characters166
Distinct categories12 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)0.9%

Sample

1st rowToy Story Collection
2nd rowsin datos
3rd rowGrumpy Old Men Collection
4th rowsin datos
5th rowFather of the Bride Collection

Common Values

ValueCountFrequency (%)
sin datos 40891
90.1%
The Bowery Boys 29
 
0.1%
Totò Collection 27
 
0.1%
James Bond Collection 26
 
0.1%
Zatôichi: The Blind Swordsman 26
 
0.1%
The Carry On Collection 25
 
0.1%
Pokémon Collection 22
 
< 0.1%
Charlie Chan (Sidney Toler) Collection 21
 
< 0.1%
Godzilla (Showa) Collection 16
 
< 0.1%
Dragon Ball Z (Movie) Collection 15
 
< 0.1%
Other values (1686) 4281
 
9.4%

Length

2023-07-11T14:35:45.601634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sin 40893
42.3%
datos 40891
42.3%
collection 3743
 
3.9%
the 1146
 
1.2%
of 230
 
0.2%
series 147
 
0.2%
139
 
0.1%
trilogy 87
 
0.1%
and 84
 
0.1%
a 62
 
0.1%
Other values (2408) 9144
 
9.5%

Most occurring characters

ValueCountFrequency (%)
s 84370
17.8%
o 52005
10.9%
51188
10.8%
i 48450
10.2%
n 48294
10.2%
t 47379
10.0%
a 45350
9.5%
d 42284
8.9%
e 10450
 
2.2%
l 10200
 
2.1%
Other values (156) 35114
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 408231
85.9%
Space Separator 51188
 
10.8%
Uppercase Letter 13885
 
2.9%
Other Punctuation 576
 
0.1%
Open Punctuation 335
 
0.1%
Close Punctuation 335
 
0.1%
Decimal Number 321
 
0.1%
Dash Punctuation 162
 
< 0.1%
Other Letter 37
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 84370
20.7%
o 52005
12.7%
i 48450
11.9%
n 48294
11.8%
t 47379
11.6%
a 45350
11.1%
d 42284
10.4%
e 10450
 
2.6%
l 10200
 
2.5%
c 4845
 
1.2%
Other values (69) 14604
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 4474
32.2%
T 1527
 
11.0%
S 1063
 
7.7%
B 682
 
4.9%
M 630
 
4.5%
A 509
 
3.7%
D 505
 
3.6%
H 462
 
3.3%
P 432
 
3.1%
G 417
 
3.0%
Other values (33) 3184
22.9%
Other Letter
ValueCountFrequency (%)
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
Other values (4) 8
21.6%
Other Punctuation
ValueCountFrequency (%)
. 172
29.9%
' 107
18.6%
: 99
17.2%
, 79
13.7%
& 52
 
9.0%
! 35
 
6.1%
/ 21
 
3.6%
? 4
 
0.7%
* 4
 
0.7%
3
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 80
24.9%
9 64
19.9%
3 54
16.8%
0 51
15.9%
2 21
 
6.5%
8 13
 
4.0%
5 12
 
3.7%
7 11
 
3.4%
6 10
 
3.1%
4 5
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 330
98.5%
[ 5
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 330
98.5%
] 5
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 160
98.8%
2
 
1.2%
Space Separator
ValueCountFrequency (%)
51188
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 421702
88.8%
Common 52931
 
11.1%
Cyrillic 414
 
0.1%
Hiragana 15
 
< 0.1%
Hangul 10
 
< 0.1%
Katakana 9
 
< 0.1%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 84370
20.0%
o 52005
12.3%
i 48450
11.5%
n 48294
11.5%
t 47379
11.2%
a 45350
10.8%
d 42284
10.0%
e 10450
 
2.5%
l 10200
 
2.4%
c 4845
 
1.1%
Other values (70) 28075
 
6.7%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
Common
ValueCountFrequency (%)
51188
96.7%
( 330
 
0.6%
) 330
 
0.6%
. 172
 
0.3%
- 160
 
0.3%
' 107
 
0.2%
: 99
 
0.2%
1 80
 
0.2%
, 79
 
0.1%
9 64
 
0.1%
Other values (20) 322
 
0.6%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Katakana
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Han
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474370
99.8%
Cyrillic 414
 
0.1%
None 246
 
0.1%
Hiragana 15
 
< 0.1%
Punctuation 14
 
< 0.1%
Katakana 12
 
< 0.1%
Hangul 10
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 84370
17.8%
o 52005
11.0%
51188
10.8%
i 48450
10.2%
n 48294
10.2%
t 47379
10.0%
a 45350
9.6%
d 42284
8.9%
e 10450
 
2.2%
l 10200
 
2.2%
Other values (67) 34400
7.3%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
None
ValueCountFrequency (%)
é 45
18.3%
ä 40
16.3%
ô 35
14.2%
ò 28
11.4%
ö 19
7.7%
ó 14
 
5.7%
ı 14
 
5.7%
í 9
 
3.7%
á 4
 
1.6%
İ 4
 
1.6%
Other values (19) 34
13.8%
Punctuation
ValueCountFrequency (%)
9
64.3%
3
 
21.4%
2
 
14.3%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Katakana
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
CJK
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

Interactions

2023-07-11T14:35:34.011102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:20.223984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:22.168951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:24.100965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:25.880091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:27.639929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:29.815240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:31.768119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:34.276048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:20.479306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:22.506569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:24.358063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:26.100694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:27.889862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:30.058075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:32.023040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:34.520208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:20.711554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:22.746380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:24.539183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:26.324934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:28.122963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:30.308188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:32.234961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:34.749879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:20.941016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:22.948369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:24.786098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:26.550459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:28.344502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:30.561913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:32.579288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:34.971922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:21.147176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:23.178584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:25.027575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:26.766063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:28.575090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:30.844195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:32.877985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:35.208291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:21.350891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:23.376165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:25.231086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:26.969990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:28.776952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:31.077507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:33.170269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:35.426649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:21.571949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:23.588365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:25.462008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:27.210048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:29.353184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:31.283708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:33.505711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:35.653014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:21.801547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:23.811866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:25.681038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:27.418991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:29.559271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:31.524645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T14:35:33.744129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T14:35:45.803123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
budgetidrevenueruntimevote_averagevote_countrelease_yearreturnoriginal_languagestatus
budget1.000-0.2550.6440.2290.0720.4840.1410.1450.0000.000
id-0.2551.000-0.277-0.213-0.149-0.4330.392-0.2020.0710.053
revenue0.644-0.2771.0000.2550.1270.5130.1030.1660.0000.000
runtime0.229-0.2130.2551.0000.1960.2980.0320.0940.1110.000
vote_average0.072-0.1490.1270.1961.0000.318-0.0090.0630.0700.026
vote_count0.484-0.4330.5130.2980.3181.0000.1970.1500.0000.000
release_year0.1410.3920.1030.032-0.0090.1971.000-0.0830.1440.027
return0.145-0.2020.1660.0940.0630.150-0.0831.0000.0000.000
original_language0.0000.0710.0000.1110.0700.0000.1440.0001.0000.072
status0.0000.0530.0000.0000.0260.0000.0270.0000.0721.000

Missing values

2023-07-11T14:35:36.087214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T14:35:36.794177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

belongs_to_collectionbudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturnname_collection
0{'id': 10194, 'name': 'Toy Story Collection', 'poster_path': '/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg', 'backdrop_path': '/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg'}30000000.0Animation, Comedy, Family862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.946943Pixar Animation StudiosUnited States of America1995-10-30373554033.081.0EnglishReleasedsin datosToy Story7.75415.0199512.451801Toy Story Collection
1{'name':'sin datos' }65000000.0Adventure, Fantasy, Family8844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.015539TriStar Pictures, Teitler Film, Interscope CommunicationsUnited States of America1995-12-15262797249.0104.0English, FrançaisReleasedRoll the dice and unleash the excitement!Jumanji6.92413.019954.043035sin datos
2{'id': 119050, 'name': 'Grumpy Old Men Collection', 'poster_path': '/nLvUdqgPgm3F85NMCii9gVFUcet.jpg', 'backdrop_path': '/hypTnLot2z8wpFS7qwsQHW1uV8u.jpg'}0.0Romance, Comedy15602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129Warner Bros., Lancaster GateUnited States of America1995-12-220.0101.0EnglishReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.592.019950.000000Grumpy Old Men Collection
3{'name':'sin datos' }16000000.0Comedy, Drama, Romance31357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.859495Twentieth Century Fox Film CorporationUnited States of America1995-12-2281452156.0127.0EnglishReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.134.019955.090760sin datos
4{'id': 96871, 'name': 'Father of the Bride Collection', 'poster_path': '/nts4iOmNnq7GNicycMJ9pSAn204.jpg', 'backdrop_path': '/7qwE57OVZmMJChBpLEbJEmzUydk.jpg'}0.0Comedy11862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.387519Sandollar Productions, Touchstone PicturesUnited States of America1995-02-1076578911.0106.0EnglishReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.7173.019950.000000Father of the Bride Collection
5{'name':'sin datos' }60000000.0Action, Crime, Drama, Thriller949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.924927Regency Enterprises, Forward Pass, Warner Bros.United States of America1995-12-15187436818.0170.0English, EspañolReleasedA Los Angeles Crime SagaHeat7.71886.019953.123947sin datos
6{'name':'sin datos' }58000000.0Comedy, Romance11860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.677277Paramount Pictures, Scott Rudin Productions, Mirage Enterprises, Sandollar Productions, Constellation Entertainment, Worldwide, Mont Blanc Entertainment GmbHGermany, United States of America1995-12-150.0127.0Français, EnglishReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.2141.019950.000000sin datos
7{'name':'sin datos' }0.0Action, Adventure, Drama, Family45325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.561161Walt Disney PicturesUnited States of America1995-12-220.097.0English, DeutschReleasedThe Original Bad Boys.Tom and Huck5.445.019950.000000sin datos
8{'name':'sin datos' }35000000.0Action, Adventure, Thriller9091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.23158Universal Pictures, Imperial Entertainment, Signature EntertainmentUnited States of America1995-12-2264350171.0106.0EnglishReleasedTerror goes into overtime.Sudden Death5.5174.019951.838576sin datos
9{'id': 645, 'name': 'James Bond Collection', 'poster_path': '/HORpg5CSkmeQlAolx3bKMrKgfi.jpg', 'backdrop_path': '/6VcVl48kNKvdXOZfJPdarlUGOsk.jpg'}58000000.0Adventure, Action, Thriller710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.686036United Artists, Eon ProductionsUnited Kingdom, United States of America1995-11-16352194034.0130.0English, Pусский, EspañolReleasedNo limits. No fears. No substitutes.GoldenEye6.61194.019956.072311James Bond Collection
belongs_to_collectionbudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturnname_collection
45455{'name':'sin datos' }0.0sin datos67179itSentenced to life imprisonment for illegal activities, Italian International member Giulio Manieri holds on to his political ideals while struggling against madness in the loneliness of his prison cell.0.225051sin datossin datos1972-01-010.090.0ItalianoReleasedsin datosSt. Michael Had a Rooster6.03.019720.0sin datos
45456{'name':'sin datos' }0.0Horror, Mystery, Thriller84419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.222814Universal PicturesUnited States of America1946-03-290.065.0EnglishReleasedMeet...The CREEPER!House of Horrors6.38.019460.0sin datos
45457{'name':'sin datos' }0.0Mystery, Horror390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.076061sin datossin datos2000-10-220.045.0EnglishReleasedsin datosShadow of the Blair Witch7.02.020000.0sin datos
45458{'name':'sin datos' }0.0Horror289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.38645Neptune Salad Entertainment, Pirie ProductionsUnited States of America2000-10-030.030.0EnglishReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.01.020000.0sin datos
45459{'name':'sin datos' }0.0Science Fiction222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.661558Concorde-New HorizonsUnited States of America1995-01-010.085.0EnglishReleasedsin datosCaged Heat 30003.51.019950.0sin datos
45460{'name':'sin datos' }0.0Drama, Action, Romance30840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.683753Westdeutscher Rundfunk (WDR), Working Title Films, 20th Century Fox Television, CanWest Global CommunicationsCanada, Germany, United Kingdom, United States of America1991-05-130.0104.0EnglishReleasedsin datosRobin Hood5.726.019910.0sin datos
45462{'name':'sin datos' }0.0Drama111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.178241Sine OliviaPhilippines2011-11-170.0360.0sin datosReleasedsin datosCentury of Birthing9.03.020110.0sin datos
45463{'name':'sin datos' }0.0Action, Drama, Thriller67758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.903007American World PicturesUnited States of America2003-08-010.090.0EnglishReleasedA deadly game of wits.Betrayal3.86.020030.0sin datos
45464{'name':'sin datos' }0.0sin datos227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.003503YermolievRussia1917-10-210.087.0sin datosReleasedsin datosSatan Triumphant0.00.019170.0sin datos
45465{'name':'sin datos' }0.0sin datos461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.163015sin datosUnited Kingdom2017-06-090.075.0EnglishReleasedsin datosQueerama0.00.020170.0sin datos

Duplicate rows

Most frequently occurring

belongs_to_collectionbudgetgenresidoriginal_languageoverviewrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturnname_collection# duplicates
0{'id': 158365, 'name': 'Why We Fight', 'poster_path': '/fFYBLu2Hnx27CWLOMd425ExDkgK.jpg', 'backdrop_path': None}0.0Documentary159849enThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.1943-01-010.057.0EnglishReleasedsin datosWhy We Fight: Divide and Conquer5.01.019430.0Why We Fight2
1{'id': 34055, 'name': 'Pokémon Collection', 'poster_path': '/j5te0YNZAMXDBnsqTUDKIBEt8iu.jpg', 'backdrop_path': '/iGoYKA0TFfgSoZpG2u5viTJMGfK.jpg'}0.0Adventure, Fantasy, Animation, Science Fiction, Family12600jaAll your favorite Pokémon characters are back, and are joined for the first time by the legendary Pokémon Celebi and Suicune, in this latest exciting Pokémon adventure! In order to escape a greedy Pokémon hunter, Celebi must use the last of its energy to travel through time to the present day. Celebi brings along Sammy, a boy who had been trying to protect it. Along with Ash, Pikachu, and the rest of the gang, Sammy and Celebi encounter an enemy far more advanced than the hunter left behind in the past. This new enemy possesses a Pokéball called a “Dark Ball,” which transforms the Pokémon it captures into evil and far stronger creatures. When Celebi is captured, the fate of the entire forest is threatened. Let POKÉMON 4EVER transport you to a world of adventure as Ash, Suicune and the rest take action to save the day!2001-07-0628023563.075.0日本語Releasedsin datosPokémon 4Ever: Celebi - Voice of the Forest5.782.020010.0Pokémon Collection2
2{'name':'sin datos' }0.0Action, Drama, Romance, Adventure99080enOriginally called White Thunder, American producer Varick Frissell's 1931 film was inspired by his love for the Canadian Arctic Circle. Set in a beautifully black-and-white filmed Newfoundland, it is the story of a rivalry between two seal hunters that plays out on the ice floes during a hunt. Unsatisfied with the first cut, Frissell arranged for the crew to accompany an actual Newfoundland seal hunt on The SS Viking, on which an explosion of dynamite (carried regularly at the time on Arctic ships to combat ice jams) killed many members of the crew, including Frissell. The film was renamed in honor of the dead.1931-06-210.070.0EnglishReleasedActually produced during the Great Newfoundland Seal Hunt and You see the REAL thingThe Viking0.00.019310.0sin datos2
3{'name':'sin datos' }0.0Action, Horror, Science Fiction18440enWhen a comet strikes Earth and kicks up a cloud of toxic dust, hundreds of humans join the ranks of the living dead. But there's bad news for the survivors: The newly minted zombies are hell-bent on eradicating every last person from the planet. For the few human beings who remain, going head to head with the flesh-eating fiends is their only chance for long-term survival. Yet their battle will be dark and cold, with overwhelming odds.2007-01-010.089.0EnglishReleasedsin datosDays of Darkness5.05.020070.0sin datos2
4{'name':'sin datos' }0.0Adventure, Animation, Drama, Action, Foreign23305enIn feudal India, a warrior (Khan) who renounces his role as the longtime enforcer to a local lord becomes the prey in a murderous hunt through the Himalayan mountains.2001-09-230.086.0हिन्दीReleasedsin datosThe Warrior6.315.020010.0sin datos2
5{'name':'sin datos' }0.0Comedy, Drama265189svWhile holidaying in the French Alps, a Swedish family deals with acts of cowardliness as an avalanche breaks out.2014-08-151359497.0118.0Français, Norsk, svenska, EnglishReleasedsin datosForce Majeure6.8255.020140.0sin datos2
6{'name':'sin datos' }0.0Crime, Drama, Thriller5511frHitman Jef Costello is a perfectionist who always carefully plans his murders and who never gets caught.1967-10-2539481.0105.0FrançaisReleasedThere is no solitude greater than that of the SamuraiLe Samouraï7.9187.019670.0sin datos2
7{'name':'sin datos' }0.0Documentary84198enUsing personal stories, this powerful documentary illuminates the plight of the 49 million Americans struggling with food insecurity. A single mother, a small-town policeman and a farmer are among those for whom putting food on the table is a daily battle.2012-03-220.084.0EnglishReleasedOne Nation. Underfed.A Place at the Table6.97.020120.0sin datos2
8{'name':'sin datos' }0.0Drama109962enTwo literary women compete for 20 years: one writes for the critics; the other one, to get rich.1981-09-230.0115.0EnglishReleasedFrom the very beginning, they knew they'd be friends to the end. What they didn't count on was everything in between.Rich and Famous4.97.019810.0sin datos2
9{'name':'sin datos' }0.0Drama, Comedy168538enIn Zola's Paris, an ingenue arrives at a tony bordello: she's Nana, guileless, but quickly learning to use her erotic innocence to get what she wants. She's an actress for a soft-core filmmaker and soon is the most popular courtesan in Paris, parlaying this into a house, bought for her by a wealthy banker. She tosses him and takes up with her neighbor, a count of impeccable rectitude, and with the count's impressionable son. The count is soon fetching sticks like a dog and mortgaging his lands to satisfy her whims.1983-06-130.092.0sin datosReleasedsin datosNana, the True Key of Pleasure4.73.019830.0sin datos2